Consider the following data for the next three (03) items that follow : There are 90 applicants for a job. Some of them are graduates. Some of them have less than three years experience. Let G be the event that the first applicant interviewed is a graduate and T be the event that first applicant interviewed has at least 3 years experience.Number of graduates Number of non-graduates At least 3 years experience 18 9 Less than 3 years experience 36 27
What is \(P (G \cap \overline T)\) equal to?
The problem provides data about 90 job applicants, categorized by whether they are graduates and their years of experience. We are given the following data in a table format:
| At least 3 years experience (T) | Less than 3 years experience (\(\overline T\)) | Total | |
|---|---|---|---|
| Number of graduates (G) | 18 | 36 | 54 |
| Number of non-graduates (\(\overline G\)) | 9 | 27 | 36 |
| Total | 27 | 63 | 90 |
We are asked to find the probability \(P(G \cap \overline T)\). This represents the probability that the first applicant interviewed is a graduate (event G) AND has less than 3 years experience (event \(\overline T\)). The symbol \(\cap\) denotes the intersection of the events, meaning both events occur.
The term \(G \cap \overline T\) refers to the set of applicants who are both graduates and have less than 3 years of experience. Looking at the provided table:
From the table, the number of graduates with less than 3 years experience is 36.
The probability of an event is calculated as the ratio of the number of favorable outcomes to the total number of possible outcomes.
In this case:
The probability \(P(G \cap \overline T)\) is therefore:
\(P(G \cap \overline T) = \frac{\text{Number of graduates with less than 3 years experience}}{\text{Total number of applicants}}\)
\(P(G \cap \overline T) = \frac{36}{90}\)
To simplify the fraction \(\frac{36}{90}\), we can find the greatest common divisor (GCD) of 36 and 90, or simply divide by common factors:
So, \(P(G \cap \overline T) = \frac{2}{5}\).
The probability that the first applicant interviewed is a graduate and has less than 3 years experience is \(\frac{2}{5}\).
| Event | Description | Number of Applicants | Probability (Number/90) |
|---|---|---|---|
| G | Graduate | 54 | \(\frac{54}{90} = \frac{3}{5}\) |
| \(\overline G\) | Non-graduate | 36 | \(\frac{36}{90} = \frac{2}{5}\) |
| T | At least 3 years experience | 27 | \(\frac{27}{90} = \frac{3}{10}\) |
| \(\overline T\) | Less than 3 years experience | 63 | \(\frac{63}{90} = \frac{7}{10}\) |
| \(G \cap T\) | Graduate and At least 3 years experience | 18 | \(\frac{18}{90} = \frac{1}{5}\) |
| \(G \cap \overline T\) | Graduate and Less than 3 years experience | 36 | \(\frac{36}{90} = \frac{2}{5}\) |
| \(\overline G \cap T\) | Non-graduate and At least 3 years experience | 9 | \(\frac{9}{90} = \frac{1}{10}\) |
| \(\overline G \cap \overline T\) | Non-graduate and Less than 3 years experience | 27 | \(\frac{27}{90} = \frac{3}{10}\) |
The table provided is a type of contingency table, specifically a 2x2 table, used to display the frequency distribution of two variables (in this case, Graduate Status and Experience). Such tables are very useful in calculating probabilities, especially joint probabilities and marginal probabilities.
Understanding how to read and use contingency tables is fundamental for solving probability problems involving two or more categorical variables.
A and B are two events such that A̅ and B̅ are mutually exclusive. If P(A) = 0.5 and P(B) = 0.6, then what is the value of P(A|B)?
For two dependent events A and B, it is given that P(A) = 0.2 and P(B) = 0.5. If A ⊆ B, then the values of conditional probabilities P(A|B) and P(B|A) are respectively
If two dice are thrown and at least one the dice show 5, then the probability that the sum is 10 or more is
If A and B are two events such that P(A) = 0.6, P(B) = 0.5 and P(A ∩ B) = 0.4, then consider the following statements:
1. P(A̅ ∪ B) = 0.9
2. P(B̅ | A̅) = 0.6
Which of the statements is / are correct?
For two mutually exclusive events A and B, P(A) = 0.2 and P (A̅ ∩ B) = 0.3. What is P (A|(A ∪ B)) equal to?
Consider the following statements:
1. If A and B are mutually exclusive events, then it is possible that P(A) = P(B) = 0.6.
2. If A and B are any two events such that P(A|B) = 1, then P(B̅|A̅) = 1
Which of the above statement is/are correct?If A, B, C are three events, then what is the probability that at least two of these events occur together?
If 5 of a Company’s 10 delivery trucks do not meet emission standards and 3 of them are chosen for inspection, then what is the probability that none of the trucks chosen will meet emission standards?
A problem is given to three students A, B and C whose probabilities of solving the problem are \(\frac{1}{2},\frac{3}{4}\) and \(\frac{1}{4}\) respectively. What is the probability that the problem will be solved if they all solve the problem independently?
Three groups of children contain 3 girls and 1 boy; 2 girls and 2 boys: 1 girl and 3 boys. One child is selected at random from each group. The probability that the three selected consist of 1 girl and 2 boys is
A and B are two events such that A̅ and B̅ are mutually exclusive. If P(A) = 0.5 and P(B) = 0.6, then what is the value of P(A|B)?
For two dependent events A and B, it is given that P(A) = 0.2 and P(B) = 0.5. If A ⊆ B, then the values of conditional probabilities P(A|B) and P(B|A) are respectively
In a bulb factory, machines P, Q and R manufacture respectively 25%, 35% and 40% of the total. Of their output 5, 4 and 2 percent respectively are defective bulbs. A bulb is drawn at random and it is found to be defective. What is the probability that it was manufactured by machine Q?
Let X1 and X2 be independent random variables each having geometric distribution qk p ; k = 0, 1, 2, …. Then the conditional distribution of X1 given X1 + X2 is
Find the probability that they speak the same fact if A speaks truth in 60% cases and B speaks truth in 75% cases?